ArticleApplied clinical informatics2020
Accuracy of an Electronic Health Record Patient Linkage Module Evaluated between Neighboring Academic Health Care Centers.
Article in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 23 citations in OpenAlex.
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- Accuracy of an XGBoost-based privacy preserving record linkage system compared with an electronic health record patient matching module in identifying patients shared between nearby academic health centers.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A problem of Epic proportion.PLOS digital health · 2026Article
- Modernizing data and data science infrastructure as a strategic asset for cancer center.npj health systems · 2026Review
- Long-term liver outcomes after metabolic surgery in compensated cirrhosis due to metabolic dysfunction-associated steatohepatitis.Nature medicine · 2025Observational
- Delivering Guideline-Concordant Care for Patients With High-Risk HPV and Normal Cytologic Findings.JAMA network open · 2025Article
- Qualitative perspectives of Medicaid-insured patients on ambulatory care at an academic medical center: challenges and opportunities.BMC health services research · 2024Article
- Manual Evaluation of Record Linkage Algorithm Performance in Four Real-World Datasets.Applied clinical informatics · 2024Article
- A qualitative study of primary care clinician's approach to ending cervical cancer screening in older women in the United States.Preventive medicine reports · 2023Article
- Engaging Patients and Other Stakeholders in "Designing for Dissemination" of Record Linkage Methods and Tools.Applied clinical informatics · 2023Article
- Optimizing Patient Record Linkage in a Master Patient Index Using Machine Learning: Algorithm Development and Validation.JMIR formative research · 2023Article
- How Image Exchange Breaks Down: the Image Library Perspective.Journal of digital imaging · 2022Article
- Direct Secure Messaging in Practice-Recommendations for Improvements.Applied clinical informatics · 2022Article
- Article
- Alcohol use disorder treatment and outcomes among hospitalized adults with alcoholic hepatitis.Drug and alcohol dependence reports · 2021Article
- The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform.ACI open · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundPatients often seek medical treatment among different health care organizations, which can lead to redundant tests and treatments. One electronic health record (EHR) platform, Epic Systems, uses a patient linkage tool called Care Everywhere (CE), to match patients across institutions. To the extent that such linkages accurately identify shared patients across organizations, they would hold potential for improving care.
objectiveThis study aimed to understand how accurate the CE tool with default settings is to identify identical patients between two neighboring academic health care systems in Southern California, The University of California Los Angeles (UCLA) and Cedars-Sinai Medical Center.
methodsWe studied CE patient linkage queries received at UCLA from Cedars-Sinai between November 1, 2016, and April 30, 2017. We constructed datasets comprised of linkages ("successful" queries), as well as nonlinkages ("unsuccessful" queries) during this time period. To identify false positive linkages, we screened the "successful" linkages for potential errors and then manually reviewed all that screened positive. To identify false-negative linkages, we applied our own patient matching algorithm to the "unsuccessful" queries and then manually reviewed a sample to identify missed patient linkages.
resultsDuring the 6-month study period, Cedars-Sinai attempted to link 181,567 unique patient identities to records at UCLA. CE made 22,923 "successful" linkages and returned 158,644 "unsuccessful" queries among these patients. Manual review of the screened "successful" linkages between the two institutions determined there were no false positives. Manual review of a sample of the "unsuccessful" queries (
conclusionWe found that CE provided very reliable patient matching across institutions. The system missed a few linkages, but the false-negative rate was low and there were no false-positive matches over 6 months of use between two nearby institutions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.